Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Vision, instruction, and action
Vision, instruction, and action
O-Plan: the open planning architecture
Artificial Intelligence
A general programming language for unified planning and control
Artificial Intelligence - Special volume on planning and scheduling
Managing multiple tasks in complex, dynamic environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Using simulation and critical points to define states in continuous search spaces
Proceedings of the 32nd conference on Winter simulation
The Dynamic Structure of Everyday Life
The Dynamic Structure of Everyday Life
A mixed-initiative planning approach to exploratory data analysis
A mixed-initiative planning approach to exploratory data analysis
Hierarchical agent control: a framework for defining agent behavior
Proceedings of the fifth international conference on Autonomous agents
An underlying model for defeat mechanisms
Proceedings of the 32nd conference on Winter simulation
HAC: A Unified View of Reactive and Deliberative Activity
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Virtual space ontologies for scripting agents
MMAS'04 Proceedings of the First international conference on Massively Multi-Agent Systems
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Physical schemas are representations of simple physically grounded relationships and interactions such as "move," "push," and "contain." We believe they are the conceptual primitives an agent employs to understand its environment. Physical schemas can be used at varying levels of abstraction across a variety of domains. We have designed a domain-general agent simulation and control testbed based on physical schemas. If a domain can be described in physical terms as agents moving and applying force, it can be simulated in this testbed. Furthermore, we show that physical schemas can be viewed as the basis for abstract plans and a domain-general planner, GRASP. Our simulation and planning system is currently being evaluated in a continuous, dynamic, and adversarial domain based on the game of Capture the Flag. The paper concludes with an example of how GRASP was applied to the problem of Course of Action generation and evaluation.